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Statistical Computing Seminars
Latent Class Analysis in Mplus

Latent Class Analysis (LCA) is a statistical method for identifying
unmeasured class membership among subjects using categorical and/or continuous
observed variables. For example, you may wish to categorize people based on
their drinking behaviors (observations) into different types of drinkers (latent
classes). This could lead to finding categories such as abstainers, social
drinkers, and alcohol abusers. You could try to create models to predict why one
falls into particular class memberships (why do people become alcohol abusers),
and you can also seek to explore the consequences of such class memberships
(does being an alcohol abuser/not abuser predict other variables). You can even
combine latent class analysis with other techniques. For example, you might use
survival analysis to model time to first use of alcohol and find that latent
class analysis identifies a class of long term abstainers and whose survival is
modeled separately from non-abstainers. Or if you were using latent growth
curve modeling of alchohol use over time, you could apply latent class analysis
to the trajectories of alcohol use to identify classes such as abstainers, early
drinkers who taper off, and chronic alcohol abusers. LCA can be used in many
disciplines such as Health Sciences, Psychology, Education, and the Social
Sciences. Examples will be shown using Mplus version 3.